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The use of simulation techniques to investigate le system behavior is simpli ed by the large gap between processor and le speeds Adequate results can often be obtained from a simulation which concentrates on le delays and models the computation only in a gross fashion In one area, computational delays, other than those a ecting bulk transfer, have to be considered Much of the computational overhead in le operations is due to complexity in operating systems Frequently, several milliseconds can elapse between the execution of a CALL for service and the emitting of a command to actually move the disk arm Mean overhead times for these operations can sometimes be obtained from manuals, or are otherwise obtained by executing a large number (n) the disk operation in question The known disk access times can be subtracted from the elapsed time, or the CPU time measurement may be available from the system Dividing by n gives the mean CPU time for servicing the operation If no paging of code occurs, this value should have a very low standard deviation A number of simulation languages are available and have been used extensively for system simulation The author has found that simple simulations can be written rapidly on an interactive system in any higher-level language The simulation languages now commonly available use batch processing techniques When a simulation becomes large, the computing time required can become a signi cant item in the design budget, and the simulations have to be planned carefully so that they will return an adequate bene t for the expended e ort A simulation model which includes many variables will rst be tested by changing the variables one by one to select those which have a major e ect on the outcomes Then these important variables can be evaluated by having the randomizer generate test values throughout their range, while keeping the variables which had less importance constant When a desirable model has been found, the minor variables will be tested again to verify that the perceived lack of e ect is also true at the nal setting of the model variables Another approach to handle simulations of large systems is to attack the simulation hierarchically First, the cost of primitive operations is simulated, and the results are tted to a simple mathematical formulation These formulations are then used by simulations of more complex tasks Several simulation levels may be used until the needed results are obtained At each level a veri cation is done so that the parameters used do not exceed the range simulated at the lower level, since extrapolations based on tted functions are risky This scheme requires some sophistication so that the mathematical abstractions from each level are viable
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When a computation requests service from a device which is busy, the computation will be delayed Since the computation should not be abandoned, the request will be noted as an entry in a queue for the device A single process of a transaction is delayed when it requests a le operation until it gets permission to use the le The user s le requests may be issued directly by the user s programs, or indirectly by systems programs The elements that make up a queue for a device are shown in Fig 6-8
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The parameters listed are vital when systems are monitored for planned augmentation and improvement If the queue is being simulated, then not only these variables will be collected, so that the entire usage distribution can be reconstructed
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Queues and Processes One computation may initiate multiple processes which will independently go to the le system to request services The entire computation cannot proceed beyond certain joining (P) points until outstanding processes have been completed Figure 6-9 shows the progress of a small computation, which spawns processes for each le operation Processes which have to read data frequently require delay of computations Processes which write data may only prevent the nal termination of a computation
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